Overview

Dataset statistics

Number of variables21
Number of observations179078
Missing cells513118
Missing cells (%)13.6%
Duplicate rows22
Duplicate rows (%)< 0.1%
Total size in memory28.7 MiB
Average record size in memory168.0 B

Variable types

Numeric8
Categorical13

Alerts

Dataset has 22 (< 0.1%) duplicate rowsDuplicates
batsman has a high cardinality: 516 distinct valuesHigh cardinality
non_striker has a high cardinality: 511 distinct valuesHigh cardinality
bowler has a high cardinality: 405 distinct valuesHigh cardinality
player_dismissed has a high cardinality: 487 distinct valuesHigh cardinality
fielder has a high cardinality: 499 distinct valuesHigh cardinality
wide_runs is highly overall correlated with extra_runsHigh correlation
legbye_runs is highly overall correlated with extra_runsHigh correlation
batsman_runs is highly overall correlated with total_runsHigh correlation
extra_runs is highly overall correlated with wide_runs and 2 other fieldsHigh correlation
total_runs is highly overall correlated with batsman_runsHigh correlation
inning is highly overall correlated with is_super_overHigh correlation
is_super_over is highly overall correlated with inningHigh correlation
bye_runs is highly overall correlated with dismissal_kindHigh correlation
penalty_runs is highly overall correlated with extra_runs and 1 other fieldsHigh correlation
dismissal_kind is highly overall correlated with bye_runs and 1 other fieldsHigh correlation
inning is highly imbalanced (56.7%)Imbalance
is_super_over is highly imbalanced (99.4%)Imbalance
bye_runs is highly imbalanced (98.7%)Imbalance
noball_runs is highly imbalanced (98.4%)Imbalance
penalty_runs is highly imbalanced (> 99.9%)Imbalance
player_dismissed has 170244 (95.1%) missing valuesMissing
dismissal_kind has 170244 (95.1%) missing valuesMissing
fielder has 172630 (96.4%) missing valuesMissing
wide_runs has 173673 (97.0%) zerosZeros
legbye_runs has 176141 (98.4%) zerosZeros
batsman_runs has 70845 (39.6%) zerosZeros
extra_runs has 169541 (94.7%) zerosZeros
total_runs has 63002 (35.2%) zerosZeros

Reproduction

Analysis started2023-04-10 16:33:22.346136
Analysis finished2023-04-10 16:33:49.255993
Duration26.91 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

match_id
Real number (ℝ)

Distinct756
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1802.253
Minimum1
Maximum11415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-10T22:03:49.399223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1190
median379
Q3567
95-th percentile11314
Maximum11415
Range11414
Interquartile range (IQR)377

Descriptive statistics

Standard deviation3472.3228
Coefficient of variation (CV)1.9266567
Kurtosis2.2457871
Mean1802.253
Median Absolute Deviation (MAD)188
Skewness1.9963805
Sum3.2274386 × 108
Variance12057026
MonotonicityIncreasing
2023-04-10T22:03:49.598675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 267
 
0.1%
34 263
 
0.1%
534 262
 
0.1%
476 262
 
0.1%
388 261
 
0.1%
570 259
 
0.1%
190 259
 
0.1%
536 258
 
0.1%
401 258
 
0.1%
211 257
 
0.1%
Other values (746) 176472
98.5%
ValueCountFrequency (%)
1 248
0.1%
2 247
0.1%
3 218
0.1%
4 247
0.1%
5 248
0.1%
6 216
0.1%
7 254
0.1%
8 212
0.1%
9 226
0.1%
10 239
0.1%
ValueCountFrequency (%)
11415 248
0.1%
11414 239
0.1%
11413 252
0.1%
11412 237
0.1%
11347 228
0.1%
11346 235
0.1%
11345 246
0.1%
11344 224
0.1%
11343 234
0.1%
11342 249
0.1%

inning
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
92742 
2
86240 
3
 
50
4
 
38
5
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters179078
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 92742
51.8%
2 86240
48.2%
3 50
 
< 0.1%
4 38
 
< 0.1%
5 8
 
< 0.1%

Length

2023-04-10T22:03:49.783953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T22:03:49.967627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 92742
51.8%
2 86240
48.2%
3 50
 
< 0.1%
4 38
 
< 0.1%
5 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 92742
51.8%
2 86240
48.2%
3 50
 
< 0.1%
4 38
 
< 0.1%
5 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 179078
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 92742
51.8%
2 86240
48.2%
3 50
 
< 0.1%
4 38
 
< 0.1%
5 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 179078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 92742
51.8%
2 86240
48.2%
3 50
 
< 0.1%
4 38
 
< 0.1%
5 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 92742
51.8%
2 86240
48.2%
3 50
 
< 0.1%
4 38
 
< 0.1%
5 8
 
< 0.1%

batting_team
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Mumbai Indians
22619 
Kings XI Punjab
20931 
Royal Challengers Bangalore
20908 
Kolkata Knight Riders
20858 
Chennai Super Kings
19762 
Other values (10)
74000 

Length

Max length27
Median length22
Mean length17.982533
Min length13

Characters and Unicode

Total characters3220276
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunrisers Hyderabad
2nd rowSunrisers Hyderabad
3rd rowSunrisers Hyderabad
4th rowSunrisers Hyderabad
5th rowSunrisers Hyderabad

Common Values

ValueCountFrequency (%)
Mumbai Indians 22619
12.6%
Kings XI Punjab 20931
11.7%
Royal Challengers Bangalore 20908
11.7%
Kolkata Knight Riders 20858
11.6%
Chennai Super Kings 19762
11.0%
Delhi Daredevils 18786
10.5%
Rajasthan Royals 17292
9.7%
Sunrisers Hyderabad 12908
7.2%
Deccan Chargers 9034
 
5.0%
Pune Warriors 5443
 
3.0%
Other values (5) 10537
5.9%

Length

2023-04-10T22:03:50.135114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 40693
 
9.1%
mumbai 22619
 
5.1%
indians 22619
 
5.1%
xi 20931
 
4.7%
punjab 20931
 
4.7%
royal 20908
 
4.7%
challengers 20908
 
4.7%
bangalore 20908
 
4.7%
kolkata 20858
 
4.7%
knight 20858
 
4.7%
Other values (22) 213444
47.9%

Most occurring characters

ValueCountFrequency (%)
a 366154
 
11.4%
n 267743
 
8.3%
266599
 
8.3%
e 240824
 
7.5%
i 222738
 
6.9%
s 212440
 
6.6%
r 184553
 
5.7%
l 164754
 
5.1%
g 119361
 
3.7%
h 110131
 
3.4%
Other values (27) 1064979
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2487069
77.2%
Uppercase Letter 466608
 
14.5%
Space Separator 266599
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 366154
14.7%
n 267743
10.8%
e 240824
9.7%
i 222738
9.0%
s 212440
8.5%
r 184553
 
7.4%
l 164754
 
6.6%
g 119361
 
4.8%
h 110131
 
4.4%
u 93771
 
3.8%
Other values (11) 504600
20.3%
Uppercase Letter
ValueCountFrequency (%)
K 85573
18.3%
R 79830
17.1%
C 51613
11.1%
D 48515
10.4%
I 43550
9.3%
S 36150
7.7%
P 29854
 
6.4%
M 22619
 
4.8%
X 20931
 
4.5%
B 20908
 
4.5%
Other values (5) 27065
 
5.8%
Space Separator
ValueCountFrequency (%)
266599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2953677
91.7%
Common 266599
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 366154
 
12.4%
n 267743
 
9.1%
e 240824
 
8.2%
i 222738
 
7.5%
s 212440
 
7.2%
r 184553
 
6.2%
l 164754
 
5.6%
g 119361
 
4.0%
h 110131
 
3.7%
u 93771
 
3.2%
Other values (26) 971208
32.9%
Common
ValueCountFrequency (%)
266599
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3220276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 366154
 
11.4%
n 267743
 
8.3%
266599
 
8.3%
e 240824
 
7.5%
i 222738
 
6.9%
s 212440
 
6.6%
r 184553
 
5.7%
l 164754
 
5.1%
g 119361
 
3.7%
h 110131
 
3.4%
Other values (27) 1064979
33.1%

bowling_team
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Mumbai Indians
22517 
Royal Challengers Bangalore
21236 
Kolkata Knight Riders
20940 
Kings XI Punjab
20782 
Chennai Super Kings
19556 
Other values (10)
74047 

Length

Max length27
Median length22
Mean length18.003836
Min length13

Characters and Unicode

Total characters3224091
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowRoyal Challengers Bangalore
3rd rowRoyal Challengers Bangalore
4th rowRoyal Challengers Bangalore
5th rowRoyal Challengers Bangalore

Common Values

ValueCountFrequency (%)
Mumbai Indians 22517
12.6%
Royal Challengers Bangalore 21236
11.9%
Kolkata Knight Riders 20940
11.7%
Kings XI Punjab 20782
11.6%
Chennai Super Kings 19556
10.9%
Delhi Daredevils 18725
10.5%
Rajasthan Royals 17382
9.7%
Sunrisers Hyderabad 12779
7.1%
Deccan Chargers 9039
5.0%
Pune Warriors 5457
 
3.0%
Other values (5) 10665
6.0%

Length

2023-04-10T22:03:50.310562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 40338
 
9.0%
mumbai 22517
 
5.1%
indians 22517
 
5.1%
royal 21236
 
4.8%
challengers 21236
 
4.8%
bangalore 21236
 
4.8%
kolkata 20940
 
4.7%
knight 20940
 
4.7%
riders 20940
 
4.7%
xi 20782
 
4.7%
Other values (22) 213145
47.8%

Most occurring characters

ValueCountFrequency (%)
a 367329
 
11.4%
n 267509
 
8.3%
266749
 
8.3%
e 241305
 
7.5%
i 222208
 
6.9%
s 212468
 
6.6%
r 184795
 
5.7%
l 166256
 
5.2%
g 119875
 
3.7%
h 110455
 
3.4%
Other values (27) 1065142
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2490733
77.3%
Uppercase Letter 466609
 
14.5%
Space Separator 266749
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 367329
14.7%
n 267509
10.7%
e 241305
9.7%
i 222208
8.9%
s 212468
8.5%
r 184795
 
7.4%
l 166256
 
6.7%
g 119875
 
4.8%
h 110455
 
4.4%
u 93336
 
3.7%
Other values (11) 505197
20.3%
Uppercase Letter
ValueCountFrequency (%)
K 85446
18.3%
R 80483
17.2%
C 51794
11.1%
D 48452
10.4%
I 43299
9.3%
S 35878
7.7%
P 29782
 
6.4%
M 22517
 
4.8%
B 21236
 
4.6%
X 20782
 
4.5%
Other values (5) 26940
 
5.8%
Space Separator
ValueCountFrequency (%)
266749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2957342
91.7%
Common 266749
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 367329
 
12.4%
n 267509
 
9.0%
e 241305
 
8.2%
i 222208
 
7.5%
s 212468
 
7.2%
r 184795
 
6.2%
l 166256
 
5.6%
g 119875
 
4.1%
h 110455
 
3.7%
u 93336
 
3.2%
Other values (26) 971806
32.9%
Common
ValueCountFrequency (%)
266749
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3224091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 367329
 
11.4%
n 267509
 
8.3%
266749
 
8.3%
e 241305
 
7.5%
i 222208
 
6.9%
s 212468
 
6.6%
r 184795
 
5.7%
l 166256
 
5.2%
g 119875
 
3.7%
h 110455
 
3.4%
Other values (27) 1065142
33.0%

over
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.162488
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-10T22:03:50.472132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6776843
Coefficient of variation (CV)0.55869039
Kurtosis-1.1833564
Mean10.162488
Median Absolute Deviation (MAD)5
Skewness0.049017583
Sum1819878
Variance32.236099
MonotonicityNot monotonic
2023-04-10T22:03:50.621635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 9603
 
5.4%
2 9498
 
5.3%
3 9415
 
5.3%
4 9379
 
5.2%
5 9345
 
5.2%
6 9326
 
5.2%
7 9283
 
5.2%
8 9253
 
5.2%
9 9231
 
5.2%
10 9184
 
5.1%
Other values (10) 85561
47.8%
ValueCountFrequency (%)
1 9603
5.4%
2 9498
5.3%
3 9415
5.3%
4 9379
5.2%
5 9345
5.2%
6 9326
5.2%
7 9283
5.2%
8 9253
5.2%
9 9231
5.2%
10 9184
5.1%
ValueCountFrequency (%)
20 6738
3.8%
19 7866
4.4%
18 8387
4.7%
17 8648
4.8%
16 8761
4.9%
15 8900
5.0%
14 8978
5.0%
13 9073
5.1%
12 9090
5.1%
11 9120
5.1%

ball
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6155865
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-10T22:03:50.774908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.806966
Coefficient of variation (CV)0.49977119
Kurtosis-1.0831079
Mean3.6155865
Median Absolute Deviation (MAD)2
Skewness0.0961223
Sum647472
Variance3.265126
MonotonicityNot monotonic
2023-04-10T22:03:50.908185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 29047
16.2%
2 28963
16.2%
3 28878
16.1%
4 28812
16.1%
5 28720
16.0%
6 28628
16.0%
7 5113
 
2.9%
8 795
 
0.4%
9 122
 
0.1%
ValueCountFrequency (%)
1 29047
16.2%
2 28963
16.2%
3 28878
16.1%
4 28812
16.1%
5 28720
16.0%
6 28628
16.0%
7 5113
 
2.9%
8 795
 
0.4%
9 122
 
0.1%
ValueCountFrequency (%)
9 122
 
0.1%
8 795
 
0.4%
7 5113
 
2.9%
6 28628
16.0%
5 28720
16.0%
4 28812
16.1%
3 28878
16.1%
2 28963
16.2%
1 29047
16.2%

batsman
Categorical

Distinct516
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
V Kohli
 
4211
SK Raina
 
4044
RG Sharma
 
3816
S Dhawan
 
3776
G Gambhir
 
3524
Other values (511)
159707 

Length

Max length20
Median length17
Mean length9.3189672
Min length5

Characters and Unicode

Total characters1668822
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowDA Warner
2nd rowDA Warner
3rd rowDA Warner
4th rowDA Warner
5th rowDA Warner

Common Values

ValueCountFrequency (%)
V Kohli 4211
 
2.4%
SK Raina 4044
 
2.3%
RG Sharma 3816
 
2.1%
S Dhawan 3776
 
2.1%
G Gambhir 3524
 
2.0%
RV Uthappa 3492
 
1.9%
DA Warner 3398
 
1.9%
MS Dhoni 3318
 
1.9%
AM Rahane 3215
 
1.8%
CH Gayle 3131
 
1.7%
Other values (506) 143153
79.9%

Length

2023-04-10T22:03:51.086470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s 6778
 
1.8%
v 6474
 
1.8%
singh 4936
 
1.3%
da 4774
 
1.3%
sr 4683
 
1.3%
sharma 4675
 
1.3%
m 4395
 
1.2%
de 4367
 
1.2%
sk 4324
 
1.2%
kohli 4231
 
1.2%
Other values (686) 317432
86.5%

Most occurring characters

ValueCountFrequency (%)
187991
 
11.3%
a 184573
 
11.1%
i 81253
 
4.9%
n 77193
 
4.6%
h 76034
 
4.6%
r 71858
 
4.3%
e 67861
 
4.1%
S 67592
 
4.1%
l 63139
 
3.8%
s 44242
 
2.7%
Other values (44) 747086
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 970275
58.1%
Uppercase Letter 510339
30.6%
Space Separator 187991
 
11.3%
Dash Punctuation 217
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 184573
19.0%
i 81253
 
8.4%
n 77193
 
8.0%
h 76034
 
7.8%
r 71858
 
7.4%
e 67861
 
7.0%
l 63139
 
6.5%
s 44242
 
4.6%
t 37269
 
3.8%
o 37236
 
3.8%
Other values (16) 229617
23.7%
Uppercase Letter
ValueCountFrequency (%)
S 67592
13.2%
R 43865
 
8.6%
M 43029
 
8.4%
A 41775
 
8.2%
K 41319
 
8.1%
D 34993
 
6.9%
P 34684
 
6.8%
J 24265
 
4.8%
G 23797
 
4.7%
V 23047
 
4.5%
Other values (16) 131973
25.9%
Space Separator
ValueCountFrequency (%)
187991
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1480614
88.7%
Common 188208
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 184573
 
12.5%
i 81253
 
5.5%
n 77193
 
5.2%
h 76034
 
5.1%
r 71858
 
4.9%
e 67861
 
4.6%
S 67592
 
4.6%
l 63139
 
4.3%
s 44242
 
3.0%
R 43865
 
3.0%
Other values (42) 703004
47.5%
Common
ValueCountFrequency (%)
187991
99.9%
- 217
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1668822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
187991
 
11.3%
a 184573
 
11.1%
i 81253
 
4.9%
n 77193
 
4.6%
h 76034
 
4.6%
r 71858
 
4.3%
e 67861
 
4.1%
S 67592
 
4.1%
l 63139
 
3.8%
s 44242
 
2.7%
Other values (44) 747086
44.8%

non_striker
Categorical

Distinct511
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
SK Raina
 
4173
S Dhawan
 
4090
V Kohli
 
4071
RG Sharma
 
3858
G Gambhir
 
3740
Other values (506)
159146 

Length

Max length20
Median length17
Mean length9.320648
Min length5

Characters and Unicode

Total characters1669123
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowS Dhawan
2nd rowS Dhawan
3rd rowS Dhawan
4th rowS Dhawan
5th rowS Dhawan

Common Values

ValueCountFrequency (%)
SK Raina 4173
 
2.3%
S Dhawan 4090
 
2.3%
V Kohli 4071
 
2.3%
RG Sharma 3858
 
2.2%
G Gambhir 3740
 
2.1%
AM Rahane 3467
 
1.9%
RV Uthappa 3381
 
1.9%
DA Warner 3127
 
1.7%
CH Gayle 3023
 
1.7%
AB de Villiers 2996
 
1.7%
Other values (501) 143152
79.9%

Length

2023-04-10T22:03:51.266266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s 7043
 
1.9%
v 6487
 
1.8%
sr 4897
 
1.3%
sharma 4806
 
1.3%
singh 4695
 
1.3%
m 4518
 
1.2%
da 4491
 
1.2%
sk 4423
 
1.2%
de 4315
 
1.2%
dhawan 4209
 
1.1%
Other values (684) 317210
86.4%

Most occurring characters

ValueCountFrequency (%)
188016
 
11.3%
a 185751
 
11.1%
i 81026
 
4.9%
n 77053
 
4.6%
h 75783
 
4.5%
r 71726
 
4.3%
e 68802
 
4.1%
S 67609
 
4.1%
l 62640
 
3.8%
s 44040
 
2.6%
Other values (44) 746677
44.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 970642
58.2%
Uppercase Letter 510224
30.6%
Space Separator 188016
 
11.3%
Dash Punctuation 241
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 185751
19.1%
i 81026
 
8.3%
n 77053
 
7.9%
h 75783
 
7.8%
r 71726
 
7.4%
e 68802
 
7.1%
l 62640
 
6.5%
s 44040
 
4.5%
t 36629
 
3.8%
o 35874
 
3.7%
Other values (16) 231318
23.8%
Uppercase Letter
ValueCountFrequency (%)
S 67609
13.3%
R 43936
 
8.6%
M 43729
 
8.6%
A 41719
 
8.2%
K 41240
 
8.1%
P 34420
 
6.7%
D 34226
 
6.7%
J 24292
 
4.8%
G 24129
 
4.7%
V 23253
 
4.6%
Other values (16) 131671
25.8%
Space Separator
ValueCountFrequency (%)
188016
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1480866
88.7%
Common 188257
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 185751
 
12.5%
i 81026
 
5.5%
n 77053
 
5.2%
h 75783
 
5.1%
r 71726
 
4.8%
e 68802
 
4.6%
S 67609
 
4.6%
l 62640
 
4.2%
s 44040
 
3.0%
R 43936
 
3.0%
Other values (42) 702500
47.4%
Common
ValueCountFrequency (%)
188016
99.9%
- 241
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1669123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
188016
 
11.3%
a 185751
 
11.1%
i 81026
 
4.9%
n 77053
 
4.6%
h 75783
 
4.5%
r 71726
 
4.3%
e 68802
 
4.1%
S 67609
 
4.1%
l 62640
 
3.8%
s 44040
 
2.6%
Other values (44) 746677
44.7%

bowler
Categorical

Distinct405
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Harbhajan Singh
 
3451
A Mishra
 
3172
PP Chawla
 
3157
R Ashwin
 
3016
SL Malinga
 
2974
Other values (400)
163308 

Length

Max length17
Median length16
Mean length9.4648366
Min length5

Characters and Unicode

Total characters1694944
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTS Mills
2nd rowTS Mills
3rd rowTS Mills
4th rowTS Mills
5th rowTS Mills

Common Values

ValueCountFrequency (%)
Harbhajan Singh 3451
 
1.9%
A Mishra 3172
 
1.8%
PP Chawla 3157
 
1.8%
R Ashwin 3016
 
1.7%
SL Malinga 2974
 
1.7%
DJ Bravo 2711
 
1.5%
B Kumar 2707
 
1.5%
P Kumar 2637
 
1.5%
UT Yadav 2605
 
1.5%
SP Narine 2600
 
1.5%
Other values (395) 150048
83.8%

Length

2023-04-10T22:03:51.439598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 9707
 
2.7%
singh 9243
 
2.5%
sharma 9188
 
2.5%
a 8586
 
2.4%
kumar 7561
 
2.1%
s 6896
 
1.9%
m 6348
 
1.7%
p 5150
 
1.4%
pp 5102
 
1.4%
b 4200
 
1.2%
Other values (559) 292640
80.3%

Most occurring characters

ValueCountFrequency (%)
a 217289
 
12.8%
185543
 
10.9%
n 90857
 
5.4%
r 89577
 
5.3%
h 87198
 
5.1%
i 74125
 
4.4%
e 71636
 
4.2%
S 66424
 
3.9%
l 54776
 
3.2%
M 45498
 
2.7%
Other values (45) 712021
42.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1032578
60.9%
Uppercase Letter 476162
28.1%
Space Separator 185543
 
10.9%
Dash Punctuation 586
 
< 0.1%
Open Punctuation 25
 
< 0.1%
Decimal Number 25
 
< 0.1%
Close Punctuation 25
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 217289
21.0%
n 90857
 
8.8%
r 89577
 
8.7%
h 87198
 
8.4%
i 74125
 
7.2%
e 71636
 
6.9%
l 54776
 
5.3%
o 39839
 
3.9%
t 39039
 
3.8%
m 38850
 
3.8%
Other values (16) 229392
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 66424
13.9%
M 45498
 
9.6%
A 41882
 
8.8%
P 40956
 
8.6%
K 35273
 
7.4%
R 33569
 
7.0%
J 31117
 
6.5%
B 25271
 
5.3%
D 22315
 
4.7%
C 19224
 
4.0%
Other values (14) 114633
24.1%
Space Separator
ValueCountFrequency (%)
185543
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 586
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Decimal Number
ValueCountFrequency (%)
2 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1508740
89.0%
Common 186204
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 217289
 
14.4%
n 90857
 
6.0%
r 89577
 
5.9%
h 87198
 
5.8%
i 74125
 
4.9%
e 71636
 
4.7%
S 66424
 
4.4%
l 54776
 
3.6%
M 45498
 
3.0%
A 41882
 
2.8%
Other values (40) 669478
44.4%
Common
ValueCountFrequency (%)
185543
99.6%
- 586
 
0.3%
( 25
 
< 0.1%
2 25
 
< 0.1%
) 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1694944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 217289
 
12.8%
185543
 
10.9%
n 90857
 
5.4%
r 89577
 
5.3%
h 87198
 
5.1%
i 74125
 
4.4%
e 71636
 
4.2%
S 66424
 
3.9%
l 54776
 
3.2%
M 45498
 
2.7%
Other values (45) 712021
42.0%

is_super_over
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
178997 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters179078
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 178997
> 99.9%
1 81
 
< 0.1%

Length

2023-04-10T22:03:51.599137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T22:03:51.756864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 178997
> 99.9%
1 81
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 178997
> 99.9%
1 81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 179078
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 178997
> 99.9%
1 81
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 179078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 178997
> 99.9%
1 81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 178997
> 99.9%
1 81
 
< 0.1%

wide_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.036721429
Minimum0
Maximum5
Zeros173673
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-10T22:03:51.869987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25116113
Coefficient of variation (CV)6.839634
Kurtosis191.68588
Mean0.036721429
Median Absolute Deviation (MAD)0
Skewness11.663078
Sum6576
Variance0.063081914
MonotonicityNot monotonic
2023-04-10T22:03:52.003801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 173673
97.0%
1 4915
 
2.7%
2 230
 
0.1%
5 208
 
0.1%
3 47
 
< 0.1%
4 5
 
< 0.1%
ValueCountFrequency (%)
0 173673
97.0%
1 4915
 
2.7%
2 230
 
0.1%
3 47
 
< 0.1%
4 5
 
< 0.1%
5 208
 
0.1%
ValueCountFrequency (%)
5 208
 
0.1%
4 5
 
< 0.1%
3 47
 
< 0.1%
2 230
 
0.1%
1 4915
 
2.7%
0 173673
97.0%

bye_runs
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
178598 
1
 
324
4
 
123
2
 
31
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters179078
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 178598
99.7%
1 324
 
0.2%
4 123
 
0.1%
2 31
 
< 0.1%
3 2
 
< 0.1%

Length

2023-04-10T22:03:52.155554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T22:03:52.325505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 178598
99.7%
1 324
 
0.2%
4 123
 
0.1%
2 31
 
< 0.1%
3 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 178598
99.7%
1 324
 
0.2%
4 123
 
0.1%
2 31
 
< 0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 179078
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 178598
99.7%
1 324
 
0.2%
4 123
 
0.1%
2 31
 
< 0.1%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 179078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 178598
99.7%
1 324
 
0.2%
4 123
 
0.1%
2 31
 
< 0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 178598
99.7%
1 324
 
0.2%
4 123
 
0.1%
2 31
 
< 0.1%
3 2
 
< 0.1%

legbye_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021136041
Minimum0
Maximum5
Zeros176141
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-10T22:03:52.455365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1949083
Coefficient of variation (CV)9.2216086
Kurtosis242.32652
Mean0.021136041
Median Absolute Deviation (MAD)0
Skewness13.777287
Sum3785
Variance0.037989245
MonotonicityNot monotonic
2023-04-10T22:03:52.591956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 176141
98.4%
1 2558
 
1.4%
4 220
 
0.1%
2 138
 
0.1%
3 17
 
< 0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
0 176141
98.4%
1 2558
 
1.4%
2 138
 
0.1%
3 17
 
< 0.1%
4 220
 
0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
5 4
 
< 0.1%
4 220
 
0.1%
3 17
 
< 0.1%
2 138
 
0.1%
1 2558
 
1.4%
0 176141
98.4%

noball_runs
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
178364 
1
 
698
2
 
9
5
 
6
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters179078
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 178364
99.6%
1 698
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Length

2023-04-10T22:03:52.744378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T22:03:52.915673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 178364
99.6%
1 698
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 178364
99.6%
1 698
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 179078
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 178364
99.6%
1 698
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 179078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 178364
99.6%
1 698
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 178364
99.6%
1 698
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

penalty_runs
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
179076 
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters179078
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 179076
> 99.9%
5 2
 
< 0.1%

Length

2023-04-10T22:03:53.231641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T22:03:53.388909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 179076
> 99.9%
5 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 179076
> 99.9%
5 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 179078
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 179076
> 99.9%
5 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 179078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 179076
> 99.9%
5 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 179076
> 99.9%
5 2
 
< 0.1%

batsman_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2468645
Minimum0
Maximum7
Zeros70845
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-10T22:03:53.506067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6082703
Coefficient of variation (CV)1.2898517
Kurtosis1.6326929
Mean1.2468645
Median Absolute Deviation (MAD)1
Skewness1.5825227
Sum223286
Variance2.5865332
MonotonicityNot monotonic
2023-04-10T22:03:53.638043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 70845
39.6%
1 67523
37.7%
4 20392
 
11.4%
2 11471
 
6.4%
6 8170
 
4.6%
3 587
 
0.3%
5 79
 
< 0.1%
7 11
 
< 0.1%
ValueCountFrequency (%)
0 70845
39.6%
1 67523
37.7%
2 11471
 
6.4%
3 587
 
0.3%
4 20392
 
11.4%
5 79
 
< 0.1%
6 8170
 
4.6%
7 11
 
< 0.1%
ValueCountFrequency (%)
7 11
 
< 0.1%
6 8170
 
4.6%
5 79
 
< 0.1%
4 20392
 
11.4%
3 587
 
0.3%
2 11471
 
6.4%
1 67523
37.7%
0 70845
39.6%

extra_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.067032243
Minimum0
Maximum7
Zeros169541
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-10T22:03:53.780515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34255293
Coefficient of variation (CV)5.1102711
Kurtosis91.227968
Mean0.067032243
Median Absolute Deviation (MAD)0
Skewness8.2341627
Sum12004
Variance0.11734251
MonotonicityNot monotonic
2023-04-10T22:03:53.908805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 169541
94.7%
1 8495
 
4.7%
2 407
 
0.2%
4 348
 
0.2%
5 219
 
0.1%
3 67
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 169541
94.7%
1 8495
 
4.7%
2 407
 
0.2%
3 67
 
< 0.1%
4 348
 
0.2%
5 219
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 219
 
0.1%
4 348
 
0.2%
3 67
 
< 0.1%
2 407
 
0.2%
1 8495
 
4.7%
0 169541
94.7%

total_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3138967
Minimum0
Maximum10
Zeros63002
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-10T22:03:54.059156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6054216
Coefficient of variation (CV)1.2218781
Kurtosis1.6401385
Mean1.3138967
Median Absolute Deviation (MAD)1
Skewness1.5569328
Sum235290
Variance2.5773787
MonotonicityNot monotonic
2023-04-10T22:03:54.199535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 73059
40.8%
0 63002
35.2%
4 20599
 
11.5%
2 13125
 
7.3%
6 8148
 
4.5%
3 688
 
0.4%
5 339
 
0.2%
8 64
 
< 0.1%
7 38
 
< 0.1%
10 16
 
< 0.1%
ValueCountFrequency (%)
0 63002
35.2%
1 73059
40.8%
2 13125
 
7.3%
3 688
 
0.4%
4 20599
 
11.5%
5 339
 
0.2%
6 8148
 
4.5%
7 38
 
< 0.1%
8 64
 
< 0.1%
10 16
 
< 0.1%
ValueCountFrequency (%)
10 16
 
< 0.1%
8 64
 
< 0.1%
7 38
 
< 0.1%
6 8148
 
4.5%
5 339
 
0.2%
4 20599
 
11.5%
3 688
 
0.4%
2 13125
 
7.3%
1 73059
40.8%
0 63002
35.2%

player_dismissed
Categorical

HIGH CARDINALITY  MISSING 

Distinct487
Distinct (%)5.5%
Missing170244
Missing (%)95.1%
Memory size1.4 MiB
SK Raina
 
162
RG Sharma
 
155
RV Uthappa
 
153
V Kohli
 
143
S Dhawan
 
137
Other values (482)
8084 

Length

Max length20
Median length17
Mean length9.3534073
Min length5

Characters and Unicode

Total characters82628
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)0.9%

Sample

1st rowDA Warner
2nd rowS Dhawan
3rd rowMC Henriques
4th rowYuvraj Singh
5th rowMandeep Singh

Common Values

ValueCountFrequency (%)
SK Raina 162
 
0.1%
RG Sharma 155
 
0.1%
RV Uthappa 153
 
0.1%
V Kohli 143
 
0.1%
S Dhawan 137
 
0.1%
G Gambhir 136
 
0.1%
KD Karthik 135
 
0.1%
PA Patel 126
 
0.1%
AM Rahane 116
 
0.1%
AT Rayudu 115
 
0.1%
Other values (477) 7456
 
4.2%
(Missing) 170244
95.1%

Length

2023-04-10T22:03:54.367884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
singh 316
 
1.7%
s 311
 
1.7%
v 261
 
1.4%
r 246
 
1.4%
m 241
 
1.3%
sharma 237
 
1.3%
sk 189
 
1.0%
patel 189
 
1.0%
sr 184
 
1.0%
de 175
 
1.0%
Other values (653) 15744
87.0%

Most occurring characters

ValueCountFrequency (%)
a 9407
 
11.4%
9259
 
11.2%
i 3946
 
4.8%
h 3889
 
4.7%
n 3835
 
4.6%
r 3632
 
4.4%
e 3337
 
4.0%
S 3271
 
4.0%
l 2975
 
3.6%
M 2129
 
2.6%
Other values (44) 36948
44.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48396
58.6%
Uppercase Letter 24951
30.2%
Space Separator 9259
 
11.2%
Dash Punctuation 22
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9407
19.4%
i 3946
 
8.2%
h 3889
 
8.0%
n 3835
 
7.9%
r 3632
 
7.5%
e 3337
 
6.9%
l 2975
 
6.1%
s 2067
 
4.3%
t 1920
 
4.0%
o 1860
 
3.8%
Other values (16) 11528
23.8%
Uppercase Letter
ValueCountFrequency (%)
S 3271
13.1%
M 2129
 
8.5%
A 2123
 
8.5%
R 2111
 
8.5%
K 1943
 
7.8%
P 1810
 
7.3%
D 1552
 
6.2%
J 1250
 
5.0%
V 1083
 
4.3%
G 1070
 
4.3%
Other values (16) 6609
26.5%
Space Separator
ValueCountFrequency (%)
9259
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73347
88.8%
Common 9281
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9407
 
12.8%
i 3946
 
5.4%
h 3889
 
5.3%
n 3835
 
5.2%
r 3632
 
5.0%
e 3337
 
4.5%
S 3271
 
4.5%
l 2975
 
4.1%
M 2129
 
2.9%
A 2123
 
2.9%
Other values (42) 34803
47.4%
Common
ValueCountFrequency (%)
9259
99.8%
- 22
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9407
 
11.4%
9259
 
11.2%
i 3946
 
4.8%
h 3889
 
4.7%
n 3835
 
4.6%
r 3632
 
4.4%
e 3337
 
4.0%
S 3271
 
4.0%
l 2975
 
3.6%
M 2129
 
2.6%
Other values (44) 36948
44.7%

dismissal_kind
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)0.1%
Missing170244
Missing (%)95.1%
Memory size1.4 MiB
caught
5348 
bowled
1581 
run out
852 
lbw
540 
stumped
 
278
Other values (4)
 
235

Length

Max length21
Median length6
Mean length6.2233416
Min length3

Characters and Unicode

Total characters54977
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcaught
2nd rowcaught
3rd rowcaught
4th rowbowled
5th rowbowled

Common Values

ValueCountFrequency (%)
caught 5348
 
3.0%
bowled 1581
 
0.9%
run out 852
 
0.5%
lbw 540
 
0.3%
stumped 278
 
0.2%
caught and bowled 211
 
0.1%
retired hurt 12
 
< 0.1%
hit wicket 10
 
< 0.1%
obstructing the field 2
 
< 0.1%
(Missing) 170244
95.1%

Length

2023-04-10T22:03:54.539348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T22:03:54.741242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
caught 5559
54.9%
bowled 1792
 
17.7%
run 852
 
8.4%
out 852
 
8.4%
lbw 540
 
5.3%
stumped 278
 
2.7%
and 211
 
2.1%
retired 12
 
0.1%
hurt 12
 
0.1%
hit 10
 
0.1%
Other values (4) 16
 
0.2%

Most occurring characters

ValueCountFrequency (%)
u 7555
13.7%
t 6739
12.3%
a 5770
10.5%
h 5583
10.2%
c 5571
10.1%
g 5561
10.1%
o 2646
 
4.8%
w 2342
 
4.3%
b 2334
 
4.2%
l 2334
 
4.2%
Other values (11) 8542
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53677
97.6%
Space Separator 1300
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 7555
14.1%
t 6739
12.6%
a 5770
10.7%
h 5583
10.4%
c 5571
10.4%
g 5561
10.4%
o 2646
 
4.9%
w 2342
 
4.4%
b 2334
 
4.3%
l 2334
 
4.3%
Other values (10) 7242
13.5%
Space Separator
ValueCountFrequency (%)
1300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53677
97.6%
Common 1300
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 7555
14.1%
t 6739
12.6%
a 5770
10.7%
h 5583
10.4%
c 5571
10.4%
g 5561
10.4%
o 2646
 
4.9%
w 2342
 
4.4%
b 2334
 
4.3%
l 2334
 
4.3%
Other values (10) 7242
13.5%
Common
ValueCountFrequency (%)
1300
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 7555
13.7%
t 6739
12.3%
a 5770
10.5%
h 5583
10.2%
c 5571
10.1%
g 5561
10.1%
o 2646
 
4.8%
w 2342
 
4.3%
b 2334
 
4.2%
l 2334
 
4.2%
Other values (11) 8542
15.5%

fielder
Categorical

HIGH CARDINALITY  MISSING 

Distinct499
Distinct (%)7.7%
Missing172630
Missing (%)96.4%
Memory size1.4 MiB
MS Dhoni
 
159
KD Karthik
 
152
RV Uthappa
 
125
SK Raina
 
115
AB de Villiers
 
114
Other values (494)
5783 

Length

Max length21
Median length20
Mean length9.4627792
Min length5

Characters and Unicode

Total characters61016
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)1.4%

Sample

1st rowMandeep Singh
2nd rowSachin Baby
3rd rowSachin Baby
4th rowDA Warner
5th rowBCJ Cutting

Common Values

ValueCountFrequency (%)
MS Dhoni 159
 
0.1%
KD Karthik 152
 
0.1%
RV Uthappa 125
 
0.1%
SK Raina 115
 
0.1%
AB de Villiers 114
 
0.1%
PA Patel 97
 
0.1%
RG Sharma 92
 
0.1%
V Kohli 90
 
0.1%
KA Pollard 85
 
< 0.1%
NV Ojha 82
 
< 0.1%
Other values (489) 5337
 
3.0%
(Missing) 172630
96.4%

Length

2023-04-10T22:03:54.953177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
singh 204
 
1.5%
r 202
 
1.5%
s 198
 
1.5%
ms 194
 
1.5%
m 192
 
1.4%
sharma 188
 
1.4%
de 169
 
1.3%
karthik 166
 
1.2%
patel 164
 
1.2%
dhoni 159
 
1.2%
Other values (618) 11488
86.2%

Most occurring characters

ValueCountFrequency (%)
a 7001
 
11.5%
6876
 
11.3%
i 3038
 
5.0%
h 2979
 
4.9%
n 2729
 
4.5%
r 2676
 
4.4%
e 2412
 
4.0%
S 2348
 
3.8%
l 2158
 
3.5%
K 1593
 
2.6%
Other values (45) 27206
44.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35853
58.8%
Uppercase Letter 18124
29.7%
Space Separator 6876
 
11.3%
Open Punctuation 76
 
0.1%
Close Punctuation 76
 
0.1%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7001
19.5%
i 3038
 
8.5%
h 2979
 
8.3%
n 2729
 
7.6%
r 2676
 
7.5%
e 2412
 
6.7%
l 2158
 
6.0%
t 1555
 
4.3%
s 1517
 
4.2%
o 1425
 
4.0%
Other values (16) 8363
23.3%
Uppercase Letter
ValueCountFrequency (%)
S 2348
13.0%
K 1593
 
8.8%
M 1557
 
8.6%
A 1526
 
8.4%
R 1482
 
8.2%
P 1365
 
7.5%
D 1237
 
6.8%
J 917
 
5.1%
B 845
 
4.7%
V 789
 
4.4%
Other values (15) 4465
24.6%
Space Separator
ValueCountFrequency (%)
6876
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53977
88.5%
Common 7039
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7001
 
13.0%
i 3038
 
5.6%
h 2979
 
5.5%
n 2729
 
5.1%
r 2676
 
5.0%
e 2412
 
4.5%
S 2348
 
4.4%
l 2158
 
4.0%
K 1593
 
3.0%
M 1557
 
2.9%
Other values (41) 25486
47.2%
Common
ValueCountFrequency (%)
6876
97.7%
( 76
 
1.1%
) 76
 
1.1%
- 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7001
 
11.5%
6876
 
11.3%
i 3038
 
5.0%
h 2979
 
4.9%
n 2729
 
4.5%
r 2676
 
4.4%
e 2412
 
4.0%
S 2348
 
3.8%
l 2158
 
3.5%
K 1593
 
2.6%
Other values (45) 27206
44.6%

Interactions

2023-04-10T22:03:44.832610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:32.610129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:34.419493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:36.141206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:37.855200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:39.517634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:41.326362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:43.042163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:45.062243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:32.833776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:34.631000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:36.349362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:38.057960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:39.720406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:41.537555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:43.248698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:45.282557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:33.049941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:34.851166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:36.566887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:38.273085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:39.934866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:41.758684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:43.465214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:45.495951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:33.263888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:35.074003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:36.787129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:38.485522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:40.308426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:41.981035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:43.684705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:45.700083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:33.467776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:35.282451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:36.994043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:38.687378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:40.507312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:42.190871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:43.896414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:45.906240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:33.789327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:35.493543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:37.203423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:38.890052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:40.705919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:42.399380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:44.124104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:46.116276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:34.000076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:35.712856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:37.422452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:39.104808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:40.916021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:42.617534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:44.360779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:46.331764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:34.214189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:35.933570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:37.641197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:39.316728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:41.124778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:42.835789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-10T22:03:44.602384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-10T22:03:55.129840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
match_idoverballwide_runslegbye_runsbatsman_runsextra_runstotal_runsinningbatting_teambowling_teamis_super_overbye_runsnoball_runspenalty_runsdismissal_kind
match_id1.0000.008-0.002-0.006-0.0130.034-0.0160.0220.0170.2910.2940.0090.0030.0010.0000.070
over0.0081.000-0.007-0.0070.0010.1330.0050.1320.0460.0000.0000.0610.0260.0180.0000.070
ball-0.002-0.0071.000-0.005-0.0060.011-0.0050.0090.0000.0000.0000.0000.0040.0020.0000.022
wide_runs-0.006-0.007-0.0051.000-0.023-0.1560.7430.0780.0000.0050.0080.0000.0000.0020.0460.231
legbye_runs-0.0130.001-0.006-0.0231.000-0.1200.5450.0580.0000.0000.0040.0000.0000.0000.0000.260
batsman_runs0.0340.1330.011-0.156-0.1201.000-0.1960.9450.0060.0150.0140.0100.0250.0640.0000.344
extra_runs-0.0160.005-0.0050.7430.545-0.1961.0000.1180.0000.0050.0080.0050.3520.1890.7090.231
total_runs0.0220.1320.0090.0780.0580.9450.1181.0000.0060.0140.0120.0140.1530.1670.1210.245
inning0.0170.0460.0000.0000.0000.0060.0000.0061.0000.0480.0550.9620.0030.0070.0000.015
batting_team0.2910.0000.0000.0050.0000.0150.0050.0140.0481.0000.1210.0140.0040.0030.0040.030
bowling_team0.2940.0000.0000.0080.0040.0140.0080.0120.0550.1211.0000.0120.0030.0040.0000.027
is_super_over0.0090.0610.0000.0000.0000.0100.0050.0140.9620.0140.0121.0000.0020.0150.0000.000
bye_runs0.0030.0260.0040.0000.0000.0250.3520.1530.0030.0040.0030.0021.0000.0000.0001.000
noball_runs0.0010.0180.0020.0020.0000.0640.1890.1670.0070.0030.0040.0150.0001.0000.0000.048
penalty_runs0.0000.0000.0000.0460.0000.0000.7090.1210.0000.0040.0000.0000.0000.0001.0001.000
dismissal_kind0.0700.0700.0220.2310.2600.3440.2310.2450.0150.0300.0270.0001.0000.0481.0001.000

Missing values

2023-04-10T22:03:46.980760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-10T22:03:47.841262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-10T22:03:48.973081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
011Sunrisers HyderabadRoyal Challengers Bangalore11DA WarnerS DhawanTS Mills000000000NaNNaNNaN
111Sunrisers HyderabadRoyal Challengers Bangalore12DA WarnerS DhawanTS Mills000000000NaNNaNNaN
211Sunrisers HyderabadRoyal Challengers Bangalore13DA WarnerS DhawanTS Mills000000404NaNNaNNaN
311Sunrisers HyderabadRoyal Challengers Bangalore14DA WarnerS DhawanTS Mills000000000NaNNaNNaN
411Sunrisers HyderabadRoyal Challengers Bangalore15DA WarnerS DhawanTS Mills020000022NaNNaNNaN
511Sunrisers HyderabadRoyal Challengers Bangalore16S DhawanDA WarnerTS Mills000000000NaNNaNNaN
611Sunrisers HyderabadRoyal Challengers Bangalore17S DhawanDA WarnerTS Mills000100011NaNNaNNaN
711Sunrisers HyderabadRoyal Challengers Bangalore21S DhawanDA WarnerA Choudhary000000101NaNNaNNaN
811Sunrisers HyderabadRoyal Challengers Bangalore22DA WarnerS DhawanA Choudhary000000404NaNNaNNaN
911Sunrisers HyderabadRoyal Challengers Bangalore23DA WarnerS DhawanA Choudhary000010011NaNNaNNaN
match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
179068114152Chennai Super KingsMumbai Indians193RA JadejaSR WatsonJJ Bumrah000000202NaNNaNNaN
179069114152Chennai Super KingsMumbai Indians194RA JadejaSR WatsonJJ Bumrah000000000NaNNaNNaN
179070114152Chennai Super KingsMumbai Indians195RA JadejaSR WatsonJJ Bumrah000000202NaNNaNNaN
179071114152Chennai Super KingsMumbai Indians196RA JadejaSR WatsonJJ Bumrah004000448NaNNaNNaN
179072114152Chennai Super KingsMumbai Indians201SR WatsonRA JadejaSL Malinga000000101NaNNaNNaN
179073114152Chennai Super KingsMumbai Indians202RA JadejaSR WatsonSL Malinga000000101NaNNaNNaN
179074114152Chennai Super KingsMumbai Indians203SR WatsonRA JadejaSL Malinga000000202NaNNaNNaN
179075114152Chennai Super KingsMumbai Indians204SR WatsonRA JadejaSL Malinga000000101SR Watsonrun outKH Pandya
179076114152Chennai Super KingsMumbai Indians205SN ThakurRA JadejaSL Malinga000000202NaNNaNNaN
179077114152Chennai Super KingsMumbai Indians206SN ThakurRA JadejaSL Malinga000000000SN ThakurlbwNaN

Duplicate rows

Most frequently occurring

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder# duplicates
12113141Kolkata Knight RidersChennai Super Kings41RV UthappaKD KarthikRA Jadeja000000101NaNNaNNaN3
02211Mumbai IndiansDelhi Daredevils41SR TendulkarC MadanPJ Sangwan000000101NaNNaNNaN2
179461Rajasthan RoyalsRoyal Challengers Bangalore44AM RahaneRA TripathiUT Yadav000000404NaNNaNNaN2
279461Rajasthan RoyalsRoyal Challengers Bangalore45AM RahaneRA TripathiUT Yadav000000101NaNNaNNaN2
379461Rajasthan RoyalsRoyal Challengers Bangalore135RA TripathiAM RahaneYS Chahal000000000NaNNaNNaN2
479462Royal Challengers BangaloreRajasthan Royals101AB de VilliersMandeep SinghI Sodhi000000000NaNNaNNaN2
5111442Sunrisers HyderabadRajasthan Royals66J BairstowDA WarnerJ Archer000000404NaNNaNNaN2
6111501Royal Challengers BangaloreRajasthan Royals101PA PatelS HetmyerK Gowtham000000101NaNNaNNaN2
7111502Rajasthan RoyalsRoyal Challengers Bangalore44JC ButtlerAM RahaneN Saini000000000NaNNaNNaN2
8113111Royal Challengers BangaloreDelhi Capitals16PA PatelV KohliI Sharma000000000NaNNaNNaN2